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Marginal Contribution Feature Importance - an Axiomatic Approach for Explaining Data.

Amnon Catav1, Boyang Fu2, Yazeed Zoabi3

  • 1School of Computer Science, Tel-Aviv University, Tel-Aviv, Israel.

Proceedings of Machine Learning Research
|September 27, 2021
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Summary
This summary is machine-generated.

New feature importance methods are needed for explaining real-world data, not just models. The study introduces Marginal Contribution Feature Importance (MCI), a novel score that accurately reflects feature contributions, especially with correlated data.

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Area of Science:

  • Machine Learning
  • Data Science
  • Statistical Modeling

Background:

  • Feature importance scores are crucial for understanding model behavior and real-world phenomena.
  • Existing methods excel at explaining models but falter when explaining data, particularly with feature correlations.

Purpose of the Study:

  • To address the limitations of current feature importance scores in data explanation.
  • To develop a theoretically sound and empirically validated feature importance score for explaining data.
  • To introduce the Marginal Contribution Feature Importance (MCI) score.

Main Methods:

  • Defined a set of axioms for desirable properties of data-explaining feature importance scores.
  • Proved the uniqueness of a score satisfying these axioms.
  • Developed and analyzed the Marginal Contribution Feature Importance (MCI) score.
  • Conducted empirical evaluations to demonstrate the score's effectiveness.

Main Results:

  • Demonstrated the limitations of existing feature importance scores when explaining data with correlated features.
  • Introduced the Marginal Contribution Feature Importance (MCI) as the unique score satisfying proposed axioms.
  • Empirically validated the merits of MCI in explaining data.

Conclusions:

  • Marginal Contribution Feature Importance (MCI) offers a robust solution for explaining data, overcoming limitations of existing model-centric approaches.
  • MCI provides a reliable method for understanding feature contributions in real-world phenomena where direct experimentation is infeasible.